I’ve just updated the section below from The Popularity of Data Analysis Software. Note that the overall article is still under construction and all the figure numbers have changed from previous versions.
Growth in Capability
The capability of analytics software has grown significantly over the years. It would be helpful to be able to plot the growth of each software package’s capabilities, but such data is hard to obtain. John Fox (2009) acquired it for R’s main distribution site http://cran.r-project.org/. I collected the data for later versions following his method.
Figure 8 shows that the growth in R packages is following a rapid parabolic arc (quadratic fit with R-squared=.998). The right-most point is for version 3.0.2, the last version released in 2013.
Figure 8. Number of R packages plotted for each major release of R.
As rapid as this growth has been, these data represent only the main CRAN repository. R does have eight other software repositories, such as the one at http://www.bioconductor.org/ that are not included in this graph. A program run on 4/7/2014 counted 7,364 R packages at all major repositories, 5,323 of which were at CRAN. So the growth curve for the software at all repositories would be roughly 38% higher on the y-axis than the one shown in Figure 8. As with any analysis software, individuals also maintain their own separate collections typically available on their web sites.
To put this astonishing growth in perspective, let us compare it to the most dominant commercial package, SAS. In version, 9.3, SAS contains around 1,200 commands that are roughly equivalent to R functions (procs, functions etc. in Base, Stat, ETS, HP Forecasting, Graph, IML, Macro, OR, QC). R packages contain a median of 5 functions (Rasmus Bååth, 12/2012 personal communication). Therefore R has approximately 36,820 functions compared to SAS’s 1,200. In fact, during 2013 alone, R added more functions/procs than SAS Institute has written in its entire history! That’s 835 packages, counting only CRAN, or around 4,175 functions. Of course these are not perfectly equivalent. Some SAS procedures have many more options to control their output than R functions do. However, R functions can nest inside one another, creating nearly infinite combinations. Also, SAS is now out with version 9.4 and I have not repeated the arduous task of recounting its commands. If SAS Institute would provide the figure, I would be happy to list it here. While the comparison is not perfect, it does provide an interesting perspective on the size and growth rate of R.
Before you can analyze data, it must be in the right form. Join me on April 25th for a 4-hour webinar that shows how to perform the most commonly used data management tasks in R. We will work through hands-on examples of R’s popular add-on packages such as plyr, reshape, stringr, lubridate and sqldf. I’ll also be presenting a 3-hour version at the UseR! 2014 conference. Here’s a list of the topics covered:
Transformation basics
Conditional transformations
Summarization of columns and rows
Summarization by group
Analysis by group
Sorting data
Selecting first or last observation per group
Miscellaneous variable tools (rename, keep, drop)
Stacking data frames
Finding and removing duplicate observations
Merging data frames
Reshaping data frames
Character string manipulations
Date / time manipulations (not in shorter useR! presentation)
Using SQL within R (not in shorter useR! presentation)
Many examples come from my books, R for SAS and SPSS Usersand R for Stata Users. That makes it easy to review what we did later with full explanations, or to learn more about a particular subject by extending an example which you have already seen.
At the end of the workshop, you will receive a set of practice exercises for you to do on your own time, as well as solutions to the problems. I will be available via email at any time in the future to address these problems or any other topics in my workshops or books. I hope to see you there!
Has learning R been driving you a bit crazy? If so, it may be that you’re “lost in translation.” On April 21 and 23, I’ll be teaching a webinar, R for SAS, SPSS and Stata Users. With each R concept, I’ll introduce it using terminology that you already know, then translate it into R’s very different view of the world. You’ll be following along, with hands-on practice, so that by the end of the workshop R’s fundamentals should be crystal clear. The examples we’ll do come right out of my books, R for SAS and SPSS Users and R for Stata Users. That way if you need more explanation later or want to dive in more deeply, the book of your choice will be very familiar. Plus, the table of contents and the index contain topics listed by SAS/SPSS/Stata terminology and R terminology so you can use either to find what you need. A complete outline of the workshop plus a registration link is here.
My latest update to The Popularity of Data Analysis Software is an attempt to use blog counts to estimate the popularity of analytics software. While I was able to greatly broaden the coverage of packages when studying job data, I made very little progress on the blog measure, adding new coverage for only Python and updating the previous counts for Stata. I post the results here mostly as a request for input from people who may know of more sources of blog lists than I have found so far.
I’ve also updated the jobs data slightly both in the main article and in the background one, How to Search for Data Science Jobs. While the changes to the search algorithm are greatly simplified, but worth reading only by people who are doing their own searches. Rather than jump through hoops to estimate total jobs for each software, I only count those for the main set of search terms. The relative results from the new search algorithm are nearly identical to the previous, more complex one (r = .99).
Here’s the update on blogs:
Blogs
On Internet blogs, people write about software that interests them, showing how to solve problems and interpreting events in the field. Blog posts contain a great deal of information about their topic, and although it’s not as time consuming as a book to write, maintaining a blog certainly requires effort. Therefore, the number of bloggers writing about analytics software has potential as a measure of popularity or market share. Unfortunately, counting the number of relevant blogs is often a difficult task. General purpose software such as Java, Python, the C language variants and MATLAB have many more bloggers writing about general programming topics than just analytics. But separating them out isn’t easy. The name of a blog and the title of its latest post may not give you a clue that it routinely includes articles on analytics.
Another problem arises from the fact that what some companies would write up as a newsletter, others would do as a set of blogs, where several people in the company each contribute their own blog, but they’re also combined into a single company blog. Statsoft and Minitab offer examples of this. What’s really interesting is not company employees who are assigned to write blogs, but rather volunteers who freely provide their time.
In a few lucky cases, lists of such blogs are maintained, usually by blog consolidators, who combine many blogs into large “metablogs.” All I have to do is find such lists and count the blogs. I don’t attempt to extract the few vendor employees that I know are blended into such lists. However, I skip those lists that are exclusively employee-based (or very close to it). The results are shown in Table 1.
Table 1. Number of blogs devoted to each software package on March 5, 2014, and the source of the data.
R’s 452 blogs is quite an impressive number. For Python, I could only find that list of 60 that were devoted to the SciPy subroutine library. Some of those are likely cover topics besides analytics, but to determine which never cover the topic would be quite time consuming. The 40 blogs about SAS is still an impressive figure given that Stata was the only other software that even garnered a list anywhere. That list is at the vendor itself, StataCorp, but it consists of non-employees except for one.
While searching for lists of blogs on other software, I did find individual blogs that at least occasionally covered a particular topic. However, keeping this list up to date is far too time consuming given the relative ease with which other popularity measures are collected.
If you know of other lists of relevant blogs, please let me know and I’ll add them. If you’re a software vendor CEO reading this, and your company does not build a metablog or at least maintain a list of your bloggers, I recommend taking advantage of this important source of free publicity.
I’m expanding the coverage of my article, The Popularity of Data Analysis Software. This is the first installment, which includes a new opening and a greatly expanded analysis of the analytics job market. Here it is, from the abstract onward through the first section…
These packages are all included in the first section on jobs, but later sections are older (each contains a date) and do not cover an as extensive set of software. I’ll add those as I can and announce the changes on Twitter where you can follow me as @BobMuenchen.
Introduction
When choosing a tool for data analysis, now more broadly referred to as analytics, there are many factors to consider. Does it run natively on your computer? Does the software provide all the methods you use? If not, how extensible is it? Does that extensibility use its own language, or an external one (e.g. Python, R) that is commonly accessible from many packages? Does it fully support the style (programming vs. point-and-click) that you like? Are its visualization options (e.g. static vs. interactive) adequate for your problems? Does it provide output in the form you prefer (e.g. cut & paste into a word processor vs. LaTeX integration)? Does it handle large enough data sets? Do your colleagues use it so you can easily share data and programs? Can you afford it?
There are many ways to measure popularity or market share and each has its advantages and disadvantages. Here they are, in approximate order of usefulness:
Job Advertisements – these are rich in information and are backed by money so they are perhaps the best measure of how popular each software is now, and what the trends are up to this point.
Published Scholarly Articles – these are also rich in information and backed by significant amounts of effort. Since a large proportion come out of academia, the source of new college graduates, they are perhaps the best measurement of new trends in analytics.
Books – the number of books that include a software’s name in its title is a particularly useful information since it requires a significant effort to write one and publishers do their own study of market share before taking the risk of publishing. However, it can be difficult to do searches to find books that use general-purpose languages which also focus only on analytics.
Blogs – the number of bloggers writing about analytics software is an interesting measure. Blog posts contain a great deal of information about their topic, and although it’s not as time consuming as a book to write, maintaining a blog certainly requires effort. What makes this measure particularly easy to gather is that consolidators like Tal Galili have created blog consolidation sites like R-Bloggers.com which make it easy to count the blogs. Previously that had been a difficult task.
Web Site Popularity – how does Google provide the most popular search results at the top of its response to your queries? A major component of that answer comes from the total number of web pages that point to any given web site. That’s known as a site’s PageRank. This is objective data, and for sites that clearly focus on analytics, it’s unbiased. However, for general-purpose software like Java, many sites that discuss programming point to http://www.java.com, and probably fewer that discuss analytics point to it as well. But it may be impractical to tell which is which.
Surveys of Use – these add additional perspective, but they are commonly done using “snowball sampling” in which the survey taker tries to widely distribute the link and then vendors vie to see who can get the most of their users to participate. So long as they all do so with equal effect, the results can be useful. However, the information is often low, because the questions are short and precise (e.g. “tools data mining” or “program languages for data mining”) and responding requires but a few mouse clicks, rather than the commitment required to place an advertisement or publish an article.
Programming Activity – some software development is focused into repositories such as GitHub. That allows people to count the number lines of programming code done for each project in a given time period. This is an excellent measure of popularity since writing programs or changing them requires substantial commitment.
Discussion Forums – these web sites or email-based discussion lists can be a very useful source of information because so many people participate, generating many tens of thousands of questions, answers and other commentary for popular software and virtually nothing for others.
Popularity Measures – some sites exist that combine several of the measures discussed here into an overall composite score or rank. In particular, they use programming activity and discussion forums.
IT Research Firms – these firms study the analytics market, interview corporate clients regarding how their needs are being met and/or changing, and write reports describing their take on where each software is now and where they’re headed.
Sales or Download Measures – the commercial analytics field has undergone a major merger and acquisition phase so that now it is hard to separate out the revenue that comes specifically from analytics. Open source software plays a major role and even the few packages that offer download figures are dicey at best.
Growth in Capability – while programming activity (mentioned above) is required before growth in capability can occur, actual growth in capability is a measure of how many new methods of analysis a software package can perform; programming activity can include routine maintenance of existing capability. Unfortunately, most software vendors don’t track this measure and, of course, simply counting the number of new things does not mean they are widely useful new things. I have only been able to collect this data for R, but the results have been very interesting.
Job Advertisements
One of the best ways to measure the popularity or market share of software for analytics is to count the number of job advertisements for each. Indeed.com is the biggest job site in the U.S. making its sample the most representative of the current job market. As their CEO and co-founder Paul Forster stated, Indeed.com includes “all the jobs from over 1,000 unique sources, comprising the major job boards – Monster, Careerbuilder, Hotjobs, Craigslist – as well as hundreds of newspapers, associations, and company websites.” To demonstrate just how dominant its lead is, a search for SPSS (on 2/19/14) showed more than ten times as many jobs on Indeed.com as on its well-known competitor, Monster.com. Indeed.com also has superb search capabilities and it even includes a tool for tracking long-term trends.
Searching for analytics jobs using Indeed.com can be easy, but it can also be very tricky. For many of the analytics software that required only a simple search on its name. However, for software that’s hard to locate (e.g. R) or that is general purpose (e.g. Java) it required complex searches and/or some rather tricky calculations which are described here. All of the graphs in this section use those procedures to make the required queries.
Figure 1a shows that Java and SAS are in a league of their own, with around 50% more analytics jobs than Python or C, C++/C# and twice as much as R. (The three aforementioned C variants are combined in a single search since job advertisements usually seek any of them). Python and C/C++/C# come next at an almost identical level of popularity. That’s not too surprising as many advertisements for analytics jobs that use programming mention both together.
Figure 1a. The number of analytics jobs for the more popular software (2/2014).
R resides in an interestingly large gap between the other domain-specific languages, SAS and SPSS. This is the first estimate I’ve done that shows that the job market for R has not only caught up with SPSS, but surpassed it by close to double the number of job postings. I knew my previous estimates for R jobs was low, but I had not yet thought of a better way to estimate the total. From SPSS on down, there’s a smooth decline. Enterprise Miner is the only data-mining-specific software to make the cutoff of at least 100 jobs. If I plotted all the software below that point, they would all pile up on the y-axis, appearing to have almost no jobs. Relatively speaking, they don’t!
Software that did not make that cut and are not displayed on the graph are: Alteryx (68), Statistica (67), RapidMiner (38), SPSS Modeler (36), KXEN (28), KNIME (26), Julia (15), Statgraphics (11), Systat (10), BMDP (8), Angos (6), Lavastorm (5), NCSS (4), Salford SPM etc. (3), Teradata Miner (2) and Oracle Data Mining (2).
It’s important to note that the values shown in Figure 1a are single points in time. The number of jobs for the more popular software do not change much from day to day, but each software has an overall trend that shows how the demand for jobs changes across the years. You can plot such trends using Indeed.com’s Job Trends tool. However, as before, focusing just on analytics jobs requires carefully constructed queries, and when comparing two trends at a time means they both have to fit in the same query limit allowed by Indeed.com. Those details are described here.
I’m particularly interested in trends involving R, so let’s look at a couple of comparisons. Figure 1b compares the number of analytics jobs available for R and SPSS across time. Analytics jobs for SPSS have not changed much over the years, while those for R have been steadily increasing. The jobs for R finally crossed over and exceeded those for SPSS toward the middle of 2012.
Figure 1b. Analytics job trends for R and SPSS. Note that the legend labels are truncated due to the very long size of the query.
We know from Figure 1a that SAS is still far ahead of R in analytics job postings. How far does R have to go to catch up with SAS? Figure 1c provides one perspective. It would be nice to have the data to forecast when R’s growth curve will catch up with SAS’s, but Indeed.com does not provide the raw data. However, we can use the approximate slope of each line to get a rough estimate. If jobs for SAS stay level and those for R continue to grow linearly as they have since January 2010, then R will catch up in 3.35 years. If instead the demand for SAS jobs that started in January of 2012 continues, then R will catch up in 1.87 years.
Figure 1c. Analytics job trends for R and SAS. Legend labels are truncated due to long query length.
A debate has been taking place on the Internet regarding the relative place of Python and R. Ironically, this debate about software to do data analytics has involved very little actual data. However it is possible now to at least study the job trends. Figure 1a showed us that Python is well out in front of R, at least on that single day the searches were run. What has the data looked like over time? The answer is in Figure 1d.
Figure 1d. Jobs trends for R and Python. Legend labels are truncated due to long query length.
Note that in this graph, Python appears to have a relatively slight advantage while in Figure 1a it had a huge one. The final point on the trend graph was done only two days after the queries used in Figure 1a, and that data changed very little in the meantime. The difference is due to the fact that Indeed.com has a limit on query length. Here is the query used for Figure 1c, and the analytic terms it contains were fewer than the one used for Figure 1a.
R
and ("big data"
or "statistical analysis"
or "data mining"
or "data analytics"
or "machine learning"
or "quantitative analysis"
or "business analytics"
or "statistical software"
or "predictive modeling")
!"R D" !"A R" !"H R" !"R N"
!toys !kids !" R Walgreen" !walmart
!"HVAC R" !"R Bard"
,
python
and ("big data"
or "statistical analysis"
or "data mining"
or "data analytics"
or "machine learning"
or "quantitative analysis"
or "business analytics"
or "statistical software"
or "predictive modeling")
The detailed description regarding the construction of all the queries used in Figures 1a through 1c is located here.
At this point, the rest of The Popularity of Data Analysis Software will continue, offering many additional perspectives on measuring analytics market share. However, until I update those sections in the coming months, they will not cover as broad a range of software. Stay tuned on Twitter, by following @BobMuenchen.
If you know SAS, SPSS or Stata and have not yet learned R, you can join me for this web-based workshop aimed at translating your knowledge into R. The next workshop begins on April 21. If you do know R and would like to learn more, you might enjoy taking Managing Data with R. The next time I’m offering that is on April 25.
If you want to learn R, or improve your current R skills, join me for two workshops that I’m offering through Revolution Analytics in January and April.
If you already know another analytics package, the workshop, Intro to R for SAS, SPSS and Stata Users may be for you. With each R concept, I’ll introduce it using terminology that you already know, then translate it into R’s very different view of the world. You’ll be following along, with hands-on practice, so that by the end of the workshop R’s fundamentals should be crystal clear. The examples we’ll do come right out of my books, R for SAS and SPSS Users and R for Stata Users. That way if you need more explanation later, or want to dive in more deeply, the book of your choice will be very familiar. Plus, the table of contents and the index contain topics listed by SAS/SPSS/Stata terminology and R terminology so you can use either to find what you need. You can see a complete out line and register for the workshop starting January 13 (click here) or April 21 (click here).
If you already know R, but want to learn more about how you can use R to get your data ready to analyze, the workshop Managing Data with R will demonstrate how to use the 15 most widely used data management tasks. The course outline and registration is available here for January and here for April.
If you have questions about any of these courses, drop me a line a muenchen.bob@gmail.com. I’m always available to answer questions regarding any of my books or workshops.
The next meeting of the Knoxville R User’s Group will consist of four 20-minute talks followed by an open planning session. It will take place on Friday, November 1, from 2:00 p.m. to 4:00 p.m. at The University of Tennessee, Haslam Business Administration Building, room 403 (1000 Volunteer Blvd., Knoxville, TN). RSVP at http://www.meetup.com/Knoxville-R-Users-Group. The topics and biographical information regarding the speakers are listed below.
Automated Forecasting using R: A Stock Market Example (2:00-2:20)
R’s forecast package can be used to generate automated ARIMA model forecasts in a method comparable to SAS Forecast Server. This talk will demonstrate how to use the R ‘quantmod’ package to query financial data from Yahoo finance and then utilize the data in the forecast package to automatically produce point forecasts and prediction intervals. Examples of how to use each package, including diagnostic plots and results, will be included.
Josh Price earned a BS and MS in statistics, both from the University of Tennessee. While working on his Master’s, he worked as a graduate assistant for Research Computing Support. After graduating, Josh worked for 7 years in industry as a consultant in both business and engineering. In January 2013, he returned to UT to work as a statistical consultant where he assists students, faculty, and staff with statistical aspects of their theses, dissertations and various research projects. Josh’s current interests include programming, forecasting methods, and quantitative finance.
BioGeoBEARS: An R package for inference and model testing in historical biogeography (2:20-2:40)
Phylogenetic biogeography is traditionally concerned with the inference of ancestral geographic ranges on a phylogeny, and of inferring the history of events that lead to present-day distributions. The field has been dominated for decades by debates about whether vicariance or dispersal is the dominant process. This talk will demonstrate, using BioGeoBEARS, that assumptions about the processes can be subject to statistical inference from the data, and show that founder-event speciation is a crucial process that has been left out of the current biogeography programs DIVA, LAGRANGE, and BayArea.
Nicholas J. Matzke is a Postdoctoral Fellow in Mathematical Biology at the National Institute for Mathematical and Biological Synthesis (NIMBioS, www.nimbios.org)) at UT Knoxville, and a member of Brian O’Meara’s lab in the Department of Ecology and Evolutionary Biology. He is also the author of the BioGeoBEARS package.
Elevating R to Supercomputers (2:40-3:00)
The biggest supercomputing platforms in the world are distributed memory machines, but the overwhelming majority of the development for parallel R infrastructure has been devoted to small shared memory machines. Additionally, most of this development focuses on task parallelism, rather than data parallelism. But as big data analytics becomes ever more attractive to both users and developers, it becomes increasingly necessary for R to add distributed computing infrastructure to support this kind of big data analytics which utilize large distributed resources. The Programming with Big Data in R (pbdR) project aims to provide such infrastructure, elevating the R language to these massive-scale computing platforms. This talk will cover some of the early successes of the pbdR project, benchmarks, challenges, and future plans.
Drew Schmidt is a researcher at the University of Tennessee’s National Institute for Computational Sciences, and is primarily interested in the intersection of mathematics, statistics, and high-performance computing. He is co-lead developer of the Programming with Big Data in R (pbdR) project, which elevates the statistics programming language R to large distributed computing platforms.
BREAK (3:00-3:10)
Analyzing Data by Group Using R’s plyr Package (3:10-3:30)
A common data analysis task is repeating the analysis for groups within your data set. In most analytics software, this is made trivial by the addition of a single statement, such as SAS’ “BY GROUP”. However, in R you must write a function and apply it by group. That function can be simple if you’re simply looking to print the results. However, if you wish to analyze those results further, you may need a series of function to apply. We’ll go over an example of each case, showing why it goes so quickly from simple to complex. This talk will use various tools from the popular plyr package to apply the functions.
Bob Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is also the creator of r4stats.com, a popular web site devoted to helping people learn R. Bob is an Accredited Professional Statistician™ with 32 years of experience and is currently the manager of OIT Research Support (formerly the Statistical Consulting Center) at the University of Tennessee. He has conducted research for a variety of public and private organizations and has assisted on more than 1,000 graduate theses and dissertations. He has written or coauthored over 60 articles published in scientific journals and conference proceedings.
Bob has served on the advisory boards of SAS Institute, SPSS Inc., the Statistical Graphics Corporation and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, JMP, STATGRAPHICS and several R packages. His research interests include statistical computing, data graphics and visualization, text analysis, data mining, psychometrics and resampling.
Quo Vadis KRUG? (3:30-4:00)
The Knoxville R User’s Group, or KRUG, started off with a series of workshops but it’s well past time to discuss where KRUGgers would like to take it. How often should we meet? How long should the talks be? Is the Friday afternoon timeslot good? Is meeting at UT sufficient, or should we move the meeting around (anyone have space?) Everything is up for discussion, so we’ll devote this final session to mull it over.
While R has more methods than any other analytics software, it has been missing a crucial feature found in most other packages. SPSS Modeler had it first, way back when they still called it Clementine. Then SAS Institute realized how crucial it was to productivity and added it to Enterprise Miner. As its reputation spread, it was added to RapidMiner, Knime,Statistica, Weka, and others. An early valiant attempt was made to add it to R. What is it? It’s the flowchart-style graphical user interface. And it will soon be available in R at last.
While menu-driven interfaces such as R Commander, Deducer or SPSS are somewhat easier to learn, the flowchart interface has two important advantages. First, you can often get a grasp of the big picture as you see steps such as separate files merging into one, or several analyses coming out of a particular data set (see figure). Second, and more important, you have a precise record of every step in your analysis. This allows you to repeat an analysis simply by changing the data inputs. Instead, menu-driven interfaces require that you switch to the programs that they create in the background if you need to automatically re-run many previous steps. That’s fine if you’re a programmer, but if you were a good programmer, you probably would not have been using that type of interface in the first place!
Alteryx’ flowchart user interface, soon to be added to Revolution R Enterprise.
This week Revolution Analytics and Alteryxannounced that future versions of Revolution R Enterprise will include Alteryx’ flowchart-style graphical user interface. Alteryx has traditionally focused on the analysis of spatial data, only adding predictive analytics in 2012 (skip 37 minutes into this presentation.) This partnership will also allow them to add Revolution’s big data features to various Alteryx products. Both companies are likely to get a significant boost in sales as a result.
While I expect both companies will benefit from this partnership, they could do much better. How? By making the Alteryx interface available for the community (free) version of R. If most R users were familiar with this interface, they would be much more likely to choose Alteryx’ tools when they needed them, instead of a competitor’s. When people needed big data tools for R, they’d be more likely to turn to Revolution Analytics. I am convinced that as great as R’s success has been, it could be greater still with a top-quality flowchart user interface that was freely available to all R users. Given the great advantages that this type of interface offers, it’s just a matter of time until a free version appears. The only question is: who will offer it?
[Update: It turns out that Alteryx is already offering a free version that works with the community version of R! See the comment from Dan Putler, product manager and one of the primary developers of Alteryx’s R-based predictive analytics and business data mining tools. I’ll be trying this out and will report my experiences in a future blog post.]
If you want to learn R, or improve your current R skills, join me for two workshops that I’m offering through Revolution Analytics in October.
If you already know another analytics package, the workshop, Intro to R for SAS, SPSS and Stata Users may be for you. With each R concept, I’ll introduce it using terminology that you already know, then translate it into R’s very different view of the world. You’ll be following along, with hands-on practice, so that by the end of the workshop R’s fundamentals should be crystal clear. The examples we’ll do come right out of my books, R for SAS and SPSS Users and R for Stata Users. That way if you need more explanation later, or want to dive in more deeply, the book of your choice will be very familiar. Plus, the table of contents and the index contain topics listed by SAS/SPSS/Stata terminology and R terminology so you can use either to find what you need. You can see a complete out line and register for the workshop here.
If you already know R, but want to learn more about data management, the workshop Managing Data with R will demonstrate how to use the 15 most widely used data management tasks. That course outline and registration is here.
If you have questions about any of these courses, drop me a line a muenchen.bob@gmail.com. I’m always available to answer questions regarding any of my books or workshops.
Tracking the job market for statistics, analytics, data mining and the like used to be a major undertaking. However, on November 10, 2011 the world’s largest web site for job postings, Indeed.com, released a tool that allows you to examine trends of your own choosing. David Smith, of Revolution Analytics, recently used this tool to compare the job markets for SAS, R, SPSS and even COBOL.
As easy as this tool is to use, some things are inherently difficult to search for. The name of the fastest growing analytics package, R, is not easy to separate from all sorts of other uses of that letter. Adding logical conditions to the search will help get a more relevant answer, but there is no perfect search for this software. For example, adding “statistics,” as David did helps a lot, but it includes jobs that use statistics (but not R) for the extremely popular job categories:
R&D = Research and Development
H.R. = Human Resources
A/R = Accounts Receivable
In studying the results of many types of searches previously, I settled on a very long query that depended on R appearing in sentences like, “the successful job applicant will have expertise in SAS, SPSS or R.” Commas are ignored in Indeed.com searches, so I used the strings “SAS R”, “R SAS”, “R or SAS”, or “SAS or R”. In addition to SAS, I used the languages: Java, Minitab, Perl, Python, Ruby, SAS, SPSS, SQL and Stata. Unfortunately Indeed’s trend tool does not allow multiple long queries. As a result, my final query is as follows:
“r sas” or “sas r” or “r or sas” or “sas or r” or “r spss” or “spss r” or “r or spss” or “spss or r” or “r stata” or “stata r” or “r or stata” or “stata or r” or “r minitab” or “minitab r” or “r or minitab” or “minitab or r”
Note the confusing use of the word “or”. Outside of quotes, it’s a logical specification as in: X or Y. Withing quotes however, it becomes part of the search string itself, where the job description includes the word “or”. From this point onward when I talk about software, “used for statistical purposes,” I am referring to this precise definition (substituting the package at hand into the query, of course).
Even with this shortened search string, only two at a time would fit into Indeed’s search. Figure 1 shows the plot comparing R and SAS.
Figure 1. The percentage of job postings across time for SAS and R. Both are focused on statistical uses via complex query.
We see that there is an overall pattern of growth for SAS. However, the growth seems to have stagnated from January, 2010 onward. At the most current time-point, the percentage of jobs for SAS is twice as high as for R. That 2-to-1 ratio is far smaller than I reported as recently as two months ago. Why the change? I had previously used complex logic to find R and simpler logic to find SAS. SAS is much easier to find, but by using simpler logic, I was essentially comparing R use for statistics to SAS use for all purposes. While that may sound like an irrelevant comparison, it is one that helps to show that R competes with SAS not just for statistical use, but also for its use in general data processing, report writing and related non-analytic tasks. Once a company is using SAS for report writing, they are more likely to use it for at least the fundamental statistics that come with Base SAS at no additional cost. Below is a graph (Fig. 2) comparing the search string “SAS !SATA !storage !firmware” to the complex R string from above. The exclamation point excludes terms, and the letters S.A.S. also stand for SCSI Attached Storage, which is related to computer firmware, not statistics. As a result, those jobs are excluded from the search.
Figure 2. Percentage of job postings across time for all uses of SAS compared to only statistical uses of R.
We see that the number of jobs for SAS is now far more dominant than before. It’s difficult to assess from the graph but a direct job search shows there are 9 times as many jobs in this type of comparison (11,320 vs. 1,246).
How much broader is the general market for SAS compared to that focused on statistics? A direct job search for SAS for all uses yields 4.5 times as many jobs as a search that focuses on SAS for only statistical purposes (11,162 vs. 2,456). Interestingly, a similar comparison for SPSS results in only a 1.8-fold difference (3,231 vs. 1,808) while one for Stata is only 1.4 times higher (897 vs. 620). The ratios may reflect the breadth of use each package has in business reporting rather than statistical analysis.
Comparing job openings of R to those for SPSS, both for statistical purposes, yields the plot in Figure 3.
Figure 3. Percentage of job postings of SPSS and R and, both for statistical purposes.
We see that both SPSS and R show an overall upward trend, with R much steeper in the more recent years. The data for the most recent time period show that SPSS is still ahead, but not by a very wide margin.
Next, let us examine the trend in jobs for R and Stata (Fig. 4).
Figure 4. Percentage of job postings across time for Stata and R, both used for statistical purposes.
We see that the jobs for Stata grew until mid-2010 where they have since been holding steady. Jobs for R have grown at much higher and steady rate since around January of 2009. In the most recent time period, there are roughly three times as many jobs for R as for Stata.
Given the power and ease of use of Indeed.com’s trend analyzer, I plan to switch the discussion over to it in future versions of The Popularity of Data Analysis Software. I’m very interested in hearing from people who can think of better ways to search for R using Indeed.com’s job trend tool.
